Many successful applications depend on statistical language models such as automatic
document classification, information retrieval, speech recognition any many
more. This thesis is focused on the task of automatic document classification, more
specifically on exploring different statistical language models that can be used to
extract features from documents. State-of-the-art methods for feature construction
are based on bag-of-words models and are largely used despite their known weaknesses.
Their popularity rests on their simplicity and often very high accuracy. With
the development of technology and machine learning algorithms, we are now able to
explore more complex methods for document representations. The goal of this thesis
is to present different document representation models that emerged in recent years
and to explore whether computational complexity of these models can be justified
by the improvement in performance. Namely, state-of-the art bag-of-word models
are used as a base for comparison of word2vec/doc2vec models and models based
on complex networks. While the bag-of-word models have been extensively studied
in the context of document classification, the other two models have not been well
understood on the same task. The study measures the performance of classifiers
trained with random forest algorithm on features generated by the specified models
tuned with different parameters. Results show that low dimensional doc2vec model
is comparable with the traditional bag-of-words model. Also, graph based models
that use selectivity measure as a feature show improvements over the bag-of-words
model on a dataset with higher number of classes.